Paper Citation Record · LEDGER

Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes

As of 16 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:1908.02419.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1908.02419 v3

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T15:13:02.118812Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

24 of 24 outbound references displayed

verified exact3
verified fuzzy10
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6e7acdae-8136-48ee-be41-b4abb108316c · outbound

This paper cites On the capabilities of multilayer perceptro ns,.

Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes On the capabilities of multilayer perceptro ns,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-14T15:13:02.006096Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a8fa5cb-bba6-4f4d-b0b6-bcc7bc81ea87 · outbound

This paper cites Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognit ion,.

Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognit ion,

Reference 2

Resolution: verified exact; doi, observed 2026-08-14T15:13:02.164268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 60e96cc1-2e27-4f84-9188-cb8af3b544f3 · outbound

This paper cites Learning capability and storage capacity of t wo- hidden-layer feedforward networks,.

Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes Learning capability and storage capacity of t wo- hidden-layer feedforward networks,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-14T15:13:02.018617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ffece35b-ebf7-4dac-b1c0-19747dbfb751 · outbound

This paper cites Bounds on the number of hidden neur ons in multilayer perceptrons,.

Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes Bounds on the number of hidden neur ons in multilayer perceptrons,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:02.537769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 54f169f9-db32-4e41-b2d9-b68399c6ee8b · outbound

This paper cites Upper bounds on the number of hid den neurons in feedforward networks with arbitrary bounded non linear activation functions,.

Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes Upper bounds on the number of hid den neurons in feedforward networks with arbitrary bounded non linear activation functions,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:02.522975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c89272af-d367-434e-a1a4-8a0b2d30b1aa · outbound

This paper cites The lower bound of the capacity for a neural network with multiple hidden layers,.

Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes The lower bound of the capacity for a neural network with multiple hidden layers,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:02.508836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a6d3ebc9-4ddb-427e-83b2-c1f5c1c1ebe1 · outbound

This paper cites Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity.

Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-14T15:13:02.038044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fab85cc0-1dd1-43bc-bdc9-fbb86d2dabca · outbound

This paper cites Identity matters in deep learning,.

Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes Identity matters in deep learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:02.494215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b8b6b97a-022d-4361-bc23-e3df696bdfe5 · outbound

This paper cites Optimization landscape and expre ssivity of deep cnns,.

Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes Optimization landscape and expre ssivity of deep cnns,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:02.481216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d9d0f5e9-4836-40af-9432-24efc8d1784e · outbound

This paper cites Hardness results for neu ral network approximation problems,.

Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes Hardness results for neu ral network approximation problems,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:02.465949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e5e950a6-84f3-46a1-afe6-79e20f099d70 · outbound

This paper cites Training a 3-node neural netwo rk is np-complete,.

Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes Training a 3-node neural netwo rk is np-complete,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:02.452598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0b66dabb-91db-4937-9b22-cb34f739d3ea · outbound

This paper cites On the comp utational efﬁciency of training neural networks,.

Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes On the comp utational efﬁciency of training neural networks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:02.438195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 95bb0f26-e34a-4e62-a4f9-c3fdf2e32521 · outbound

This paper cites Learning overparameterized neural networks via stochastic gradient descent on structured data,.

Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes Learning overparameterized neural networks via stochastic gradient descent on structured data,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-14T15:13:02.067007Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c71fdfb4-b2e5-49e9-92da-d910d6944777 · outbound

This paper cites Gradient Descent Provably Optimizes Over-parameterized Neural Networks.

Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes Gradient Descent Provably Optimizes Over-parameterized Neural Networks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-14T15:13:02.072458Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation befe36de-8c92-47f3-8e29-797b1f0a8743 · outbound

This paper cites Quadratic Suffices for Over-parametrization via Matrix Chernoff Bound.

Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes Quadratic Suffices for Over-parametrization via Matrix Chernoff Bound

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-14T15:13:02.077002Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ecdd18b-cb6c-4cef-89aa-d5f93d52993b · outbound

This paper cites A Convergence Theory for Deep Learning via Over-Parameterization.

Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes A Convergence Theory for Deep Learning via Over-Parameterization

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-14T15:13:02.081931Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation beb30428-637a-400a-b2d6-119d16b8b79b · outbound

This paper cites Gradient Descent Finds Global Minima of Deep Neural Networks.

Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes Gradient Descent Finds Global Minima of Deep Neural Networks

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-14T15:13:02.086434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7290fee-7f34-42a5-bd1d-c83a1f4073b6 · outbound

This paper cites Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks.

Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-14T15:13:02.090787Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8913bb73-3894-4f11-a7cf-03c81627f2de · outbound

This paper cites An Improved Analysis of Training Over-parameterized Deep Neural Networks.

Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes An Improved Analysis of Training Over-parameterized Deep Neural Networks

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-14T15:13:02.095110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94233533-c566-4533-bf6f-ff1440613c23 · outbound

This paper cites The Zero Set of a Real Analytic Function.

Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes The Zero Set of a Real Analytic Function

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-14T15:13:02.100067Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d949762c-1f3b-4ec1-bf96-e02f22f6de60 · outbound

This paper cites Gradien t-based learning applied to document recognition,.

Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes Gradien t-based learning applied to document recognition,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:02.418071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 392d1edb-14e8-435f-9372-d0a481192d15 · outbound

This paper cites Depth with Nonlinearity Creates No Bad Local Minima in ResNets.

Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes Depth with Nonlinearity Creates No Bad Local Minima in ResNets

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-14T15:13:02.219877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4b9c0bbf-0014-42f7-a525-d1aa559d14a0 · outbound

This paper cites Every Local Minimum Value is the Global Minimum Value of Induced Model in Non-convex Machine Learning.

Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes Every Local Minimum Value is the Global Minimum Value of Induced Model in Non-convex Machine Learning

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-14T15:13:02.196780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2e4ec549-8830-42db-a09c-4a580a7c51e2 · outbound

This paper cites Empirical margin di stributions and bounding the generalization error of combined classiﬁers,.

Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes Empirical margin di stributions and bounding the generalization error of combined classiﬁers,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:02.404253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.